Document selector_v3_windows/selector_v3_policy_vectors configs; update provenance and checksums (incl. missing selector_v2_registry.parquet checksum)

README.md

@@ -26,6 +26,14 @@ configs:
   data_files:
   - split: train
     path: data/selector_v2_registry.parquet
+- config_name: selector_v3_windows
+  data_files:
+  - split: train
+    path: data/selector_v3_windows.parquet
+- config_name: selector_v3_policy_vectors
+  data_files:
+  - split: train
+    path: data/selector_v3_policy_vectors.parquet
 ---
 
 # LLM-Serving Selector Regret
@@ -40,13 +48,15 @@ The released selector/regret and policy-suitability metrics were generated by So
 
 ## Dataset Structure
 
-This standalone dataset has two configs:
+This standalone dataset has five configs:
 
 | Config | Rows | Columns | Row meaning |
 |---|---:|---:|---|
 | `selector_regret` | 3,852 | 160 | One workload/window with context features, 27 candidate-policy reward values, oracle fields, split metadata, and regret-related decision fields. |
 | `policy_suitability` | 95,364 | 10 | One `(model_id, split, window_id, policy_name)` record for the validation-selected finalist selector. |
 | `selector_v2_registry` | 640 | 51 | One `(window, policy)` pair with 48 numerical simulation outcome metrics, 49 window features, and split metadata — training-oriented selector data for the 8-policy Option B scope. |
+| `selector_v3_windows` | 2,568 | 98 | One retained window with domain/scenario metadata, a 7-way multidomain train/OOD split, 82 engineered features (49 shared with `selector_regret` plus a new 33-column `feat_v3_*` rolling-window block), and 3 causal-discriminability labels. |
+| `selector_v3_policy_vectors` | 20,544 | 52 | One `(window_idx, policy)` pair (8 policies per window) with simulation outcome metrics, using the same 8-policy library and metric schema as `selector_v2_registry`, over a disjoint, ~32x larger, 3-domain window set. |
 
 This dataset is intentionally separate from `SoroushVahidi/llm-serving-scheduler-baselines`. That dataset contains fixed scheduler outcome rows. This dataset contains selector/oracle/regret and suitability objects that require additional intermediate features and learned-selector outputs and are not reconstructible from the scheduler-baselines release alone.
 
@@ -173,6 +183,54 @@ Generated from `llm-serving-heuristic-evolution` at commit `b698e49df7954ad6fb8e
 
 Source experiment: `selector_v2_calibrated_pilot_20260720T163235Z`. Validation manifests passed for dataset construction, quality gates (7/7), and leakage audit.
 
+## `selector_v3_windows` and `selector_v3_policy_vectors` Semantics
+
+These two configs are a same-pipeline, multi-domain scale-up of `selector_v2_registry`, generated one day later by the same experimental line (`selector_v2_overnight_20260720T235405` → `selector_v2_ood_conclusive_20260721T133408Z` → this release). They study whether a learned policy-selection model generalizes across LLM-serving trace domains (Azure-2023, BurstGPT, and synthetic stress scenarios), not just within one domain.
+
+### Relationship to `selector_v2_registry` — read this before using both
+
+`selector_v3_policy_vectors` uses the **identical metric schema and 8-policy library** as `selector_v2_registry` (same generating pipeline, commit `c8aee129f553f8dc3ede99eac60d5b14484beb41`), at roughly 32x the window count (2,568 vs. 640) and across 3 explicit source domains instead of a narrower scope.
+
+**This is not an independent metric design, and it is not a replacement.** The two configs have **zero overlapping windows** (disjoint naming schemes; 0/20,544 exact row matches against the live `selector_v2_registry` config, confirmed by direct join) and different split granularity (7-way vs. v2's 4-way). `selector_v2_registry` is **not superseded** and remains published unchanged — a reader interested in the smaller, earlier v2 window set still needs it; a reader interested in cross-domain robustness needs v3. Do not merge the two into one versioned table.
+
+**Label-vocabulary note:** `selector_v3_windows.primary_objective_classification` uses the three values `STRONGLY_DISCRIMINATIVE`, `MODERATELY_DISCRIMINATIVE`, `NEAR_TIE` — the middle category name differs from `selector_v2_registry.primary_objective_classification`'s `ALL_COMPLETE_OR_EFFECTIVELY_TIED`. Do not assume the two columns share an identical three-way taxonomy across configs.
+
+### `selector_v3_windows` row definition (2,568 rows, 98 columns)
+
+One row per retained simulated scheduling window. `window_idx` is the join key to `selector_v3_policy_vectors` (1:1).
+
+- **Domain/scenario metadata**: `source_trace` (`azure_llm_2023`, `burstgpt`, or `synthetic`), `dataset_family` (`real_trace` or `controlled_stress`), a named scenario `shape` (5 real-trace shapes plus 5 named synthetic stress scenarios: closely-spaced/same-arrival heterogeneous clusters, KV-pressure admission ordering, long-prefill overlap, admission-reorder boundary).
+- **Split**: a 7-way taxonomy — `TRAIN`, `VALIDATION`, `ROBUST_DEV`, `ID_TEST`, `CROSS_SOURCE_OOD`, `TEMPORAL_OOD`, `FINAL_OOD` — materially finer than `selector_v2_registry`'s 4-way scheme, separating "OOD" into cross-source, temporal, and held-out-final variants.
+- **Engineered features (82 columns)**: 49 columns reuse the same `feat_*` vocabulary already published in `selector_regret`; the remaining 33 columns are a new, fully-populated `feat_v3_*` block (rolling 1s/5s/20s/60s arrival/work rates, recent work/slack percentiles, negative-laxity fractions, estimated KV pressure, queue-growth-rate) with no analogue elsewhere in this dataset.
+- **Causal-discriminability labels**: `primary_objective_classification` (see label-vocabulary note above), `primary_objective_best_policy` (best of the 8 policies for this window by ANWG), `primary_objective_max_min_spread` (ANWG gap between best and worst of the 8 policies).
+
+Split distribution: `TRAIN` 968, `VALIDATION` 160, `ROBUST_DEV` 160, `ID_TEST` 312, `CROSS_SOURCE_OOD` 788, `TEMPORAL_OOD` 152, `FINAL_OOD` 28 (sums to 2,568). Domain distribution: `azure_llm_2023` 1,168 windows, `burstgpt` 760, `synthetic` 640.
+
+### `selector_v3_policy_vectors` row definition (20,544 rows, 52 columns)
+
+One row per `(window_idx, policy)` pair — exactly 8 policy rows per window, using the same 8-policy library as `selector_v2_registry`: `admission_control`, `best_fit`, `edf`, `estimated_service_time_first`, `fifo`, `multi_bin_batching`, `scorpio_style_slo_guard`, `weighted_shortest_processing`. Columns are per-(window, policy) simulator outcome metrics: goodput, latency/TTFT/TPOT/TBT percentiles, queue depths, admission/rejection, and event counts — the identical `metric_*` schema used by `selector_v2_registry`, plus one additional `domain_id` column.
+
+### Known research-conclusion limitation — `SELECTOR_STATUS = DATA_LIMITED`
+
+The experiment that produced this data self-reports `SELECTOR_STATUS = DATA_LIMITED`. Concretely, on the smallest and final held-out split (`FINAL_OOD`, n=28 windows, sourced from domains withheld from all model selection), a simple fixed Weighted-Shortest-Processing (WSP) policy baseline achieves lower mean regret (0.0008) than either learned selector variant tested — a domain-balanced random-forest selector (mean regret 0.0016) and a pessimistic-lambda selector (mean regret 0.00096). All three numbers are close and computed over only 28 windows, but as of this data, the learned causal-robustness selector has **not** been shown to beat a simple fixed baseline in the final held-out domain regime.
+
+**What this means for downstream use**: this data is suitable for studying window-level policy discriminability, engineering selector features, and comparing candidate selector designs against strong fixed baselines. It does **not**, by itself, establish that a learned selector generalizes better than WSP to genuinely unseen trace domains. Do not cite this dataset as evidence that a learned selector outperforms simple heuristics on out-of-distribution traces; the authors' own next step is to add more official-source domains before drawing that modeling conclusion.
+
+### Structural null columns (not missing data / not corruption)
+
+- **`selector_v3_policy_vectors`**: 13 of 52 columns are 100% null across all 20,544 rows (percentile-latency-breakdown, GPU-utilization, and queue-depth-mean/p95 metrics not computed by this simulator run: `metric_p50_ttft`, `metric_p50_tpot`, `metric_p99_tpot`, `metric_p50_tbt`, `metric_p99_tbt`, `metric_prefill_gpu_utilization`, `metric_decode_gpu_utilization`, `metric_prefill_queue_mean`, `metric_prefill_queue_p95`, `metric_decode_queue_mean`, `metric_decode_queue_p95`, `metric_bridge_queue_mean`, `metric_bridge_queue_p95`). A further 16 columns are null on the same 2,494/20,544 rows (12.14%) — a consistent per-row subset, not scattered missingness.
+- **`selector_v3_windows`**: 17 of 98 columns are 100% null (disaggregated-serving and multi-instance-migration features — this experiment used monolithic scheduling only, so these features are structurally inapplicable) plus 4 legacy `feat_*` columns unpopulated by this pipeline version (`feat_arrival_rate_recent`, `feat_arrival_rate_prefix`, `feat_saturation_load_estimate`, `feat_recent_slo_violation_rate`). Two columns (`time_slice_row_start`, `time_slice_row_end`) are null on exactly the 640 synthetic-domain windows, which by construction have no real-trace row range. The complete new 33-column `feat_v3_*` block is **100% populated** (0 nulls across 84,744 cells).
+
+### Intended use
+
+Selector-robustness research: training/evaluating policy-selection models under domain shift, studying which engineered features (including the new `feat_v3_*` rolling-window block) are predictive of policy discriminability, and benchmarking learned selectors against strong fixed baselines.
+
+### Provenance
+
+Generated from `llm-serving-heuristic-evolution` at commit `c8aee129f553f8dc3ede99eac60d5b14484beb41`.
+
+Source experiment: `selector_v3_multidomain_causal_20260721T151341Z`. Leakage audit passed (0 duplicate window IDs, 0 group-atomicity violations). See `metadata/provenance.json` for machine-readable provenance.
+
 ## Provenance
 
 Generated from `llm-serving-heuristic-evolution` at commit `e8bd759b6cdaa8a05096b0ceeb1c7684cfa07302`.
@@ -188,8 +246,8 @@ This release contains Soroush-generated derived metrics, features, selector outp
 Rows may be derived from or identify these upstream workload families:
 
 - **BurstGPT**: public workload trace repository, CC BY 4.0. Source: https://github.com/HPMLL/BurstGPT. If you use rows derived from BurstGPT source families, also cite the BurstGPT dataset/paper as requested by its maintainers.
-- **Azure LLM Inference Trace 2023**: public Azure trace sample, CC BY Attribution License. Source: https://github.com/Azure/AzurePublicDataset/blob/master/AzureLLMInferenceDataset2023.md. If you use rows derived from `azure_2023_*` source families, also cite the Azure trace and the Splitwise paper as requested by the dataset provider.
-- **Synthetic policy frontier**: generated by Soroush Vahidi's research workflow and covered by this dataset's license for the released metrics.
+- **Azure LLM Inference Trace 2023**: public Azure trace sample, CC BY Attribution License. Source: https://github.com/Azure/AzurePublicDataset/blob/master/AzureLLMInferenceDataset2023.md. If you use rows derived from `azure_2023_*` or `azure_llm_2023` source families, also cite the Azure trace and the Splitwise paper as requested by the dataset provider.
+- **Synthetic policy frontier / synthetic stress scenarios**: generated by Soroush Vahidi's research workflow and covered by this dataset's license for the released metrics.
 
 This dataset does not claim ownership of upstream trace data. No statement in this dataset should be read as claiming ownership of upstream BurstGPT or Azure trace data.
 
@@ -219,3 +277,5 @@ If you use, redistribute, adapt, benchmark with, or build derived artifacts from
 ## Limitations
 
 This is a decision-quality/regret benchmark, not a claim that learned top-1 policy selection is solved. Project documentation records that the selected model produces useful suitability/ranking signals but does not fully capture held-out OOD V1-to-V2 oracle gain. Users should analyze split-specific regret and OOD behavior rather than relying only on aggregate averages.
+
+The `selector_v3_windows`/`selector_v3_policy_vectors` configs carry their own open research-conclusion caveat: on the smallest held-out split (`FINAL_OOD`, n=28), a fixed WSP baseline still has lower regret than the learned selectors tested against it (see `SELECTOR_STATUS = DATA_LIMITED` above). Treat this data as suitable for feature engineering and selector-design comparison, not yet as evidence of a learned selector beating simple heuristics out-of-distribution.

SHA256SUMS

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metadata/provenance.json

@@ -290,6 +290,64 @@
         "ID_TEST": 104,
         "OOD_TEST": 144
       }
+    },
+    {
+      "config_name": "selector_v3_windows",
+      "git_sha": "c8aee129f553f8dc3ede99eac60d5b14484beb41",
+      "generating_script": "tools/selector_v3_workflow.py",
+      "source_run": "selector_v3_multidomain_causal_20260721T151341Z",
+      "rows": 2568,
+      "columns": 98,
+      "scope": "Multi-domain causal-robustness window set (azure_llm_2023, burstgpt, synthetic)",
+      "primary_objective": "arrival_normalized_weighted_goodput",
+      "policy_set": [
+        "admission_control",
+        "best_fit",
+        "edf",
+        "estimated_service_time_first",
+        "fifo",
+        "multi_bin_batching",
+        "scorpio_style_slo_guard",
+        "weighted_shortest_processing"
+      ],
+      "split_distribution": {
+        "TRAIN": 968,
+        "VALIDATION": 160,
+        "ROBUST_DEV": 160,
+        "ID_TEST": 312,
+        "CROSS_SOURCE_OOD": 788,
+        "TEMPORAL_OOD": 152,
+        "FINAL_OOD": 28
+      },
+      "domain_distribution": {
+        "azure_llm_2023": 1168,
+        "burstgpt": 760,
+        "synthetic": 640
+      },
+      "selector_status": "DATA_LIMITED",
+      "relationship_to_selector_v2_registry": "same generating pipeline and policy library, disjoint window set (0 exact row/window overlap), not a superset or replacement; both configs remain published"
+    },
+    {
+      "config_name": "selector_v3_policy_vectors",
+      "git_sha": "c8aee129f553f8dc3ede99eac60d5b14484beb41",
+      "generating_script": "tools/selector_v3_workflow.py",
+      "source_run": "selector_v3_multidomain_causal_20260721T151341Z",
+      "rows": 20544,
+      "columns": 52,
+      "scope": "Multi-domain causal-robustness policy vectors (8 policies x 2,568 windows)",
+      "primary_objective": "arrival_normalized_weighted_goodput",
+      "policy_set": [
+        "admission_control",
+        "best_fit",
+        "edf",
+        "estimated_service_time_first",
+        "fifo",
+        "multi_bin_batching",
+        "scorpio_style_slo_guard",
+        "weighted_shortest_processing"
+      ],
+      "selector_status": "DATA_LIMITED",
+      "relationship_to_selector_v2_registry": "identical metric schema and 8-policy library, disjoint window set (0 exact row/window overlap), not a superset or replacement; both configs remain published"
     }
   ]
-}
+}